Agricultural AI Global Market by Analysis & Forecast to 2021-2022: IBM, Microsoft, Granular, Descartes Labs
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The research covers the current market size of the Global Agricultural AI market and its growth rates based on 5 year history data. It also covers various types of segmentation such as by geography [United States, EU, Japan, China, India & Southeast Asia], by product /end user type [Machine Learning, Computer Vision & Predictive Analytics], by applications [Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots & Others] in overall market. The in-depth information by segments of Agricultural AI market helps monitor performance & make critical decisions for growth and profitability. It provides information on trends and developments, focuses on markets and materials, capacities, technologies, CAPEX cycle and the changing structure of the Global Agricultural AI Market.
This study also contains company profiling, product picture and specifications, sales, market share and contact information of various international, regional, and local vendors of Global Agricultural AI Market, some of them are IBM, Deere & Company, Microsoft, Agribotix, The Climate Corporation (Subsidiary of Monsanto), Granular, Descartes Labs, Prospera, Mavrx, Awhere, Gamaya, Ec2ce, Precision Hawk, Skysquirrel Technologies & Cainthus. The market competition is constantly growing higher with the rise in technological innovation and M&A activities in the industry. Moreover, many local and regional vendors are offering specific application products for varied end-users. The new vendor entrants in the market are finding it hard to compete with the international vendors based on quality, reliability, and innovations in technology.
Global Agricultural AI (Thousands Units) and Revenue (Million USD) Market Split by Product Type such as Machine Learning, Computer Vision & Predictive Analytics
Market Segment by Type 2016 2017 2018 2019 2020 2021 2022
Machine Learning xx xx xx xx xx Xx xx
-Change (%) xx% xx% xx% xx% xx% xx% xx%
Computer Vision xx xx xx xx xx Xx xx
-Change (%) xx% xx% xx% xx% xx% xx% xx%
Predictive Analytics xx xx xx xx xx Xx xx
-Change (%) xx% xx% xx% xx% xx% xx% xx%
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The research study is segmented by Application such as Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots & Others with historical and projected market share and compounded annual growth rate.
Global Agricultural AI (Thousands Units) by Application (2017-2022)
Market Segment by Application 2012 2017 2022 Market Share (%)2022 CAGR (%)
Precision Farming xx xx xx xx% xx%
Livestock Monitoring xx xx xx xx% xx%
Drone Analytics xx xx xx xx% xx%
Agriculture Robots xx xx xx xx% xx%
Others xx xx xx xx% xx%
Total xx xx xx 100% xx%
Geographically, this report is segmented into several key Regions, with production, consumption, revenue (million USD), and market share and growth rate of Agricultural AI in these regions, from 2012 to 2022 (forecast), covering
Market Segment by Regions 2012 2017 2022 Share (%) CAGR (2017-2022)
United States xx xx xx xx% xx%
EU xx xx xx xx% xx%
Japan xx xx xx xx% xx%
China xx xx xx xx% xx%
India xx xx xx xx% xx%
Southeast Asia xx xx xx xx% xx%
Total xx xx xx xx% xx%
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Key questions answered in this report - Global Agricultural AI Market Size, Status and Forecast 2022
What will the market size be in 2022 and what will the growth rate be
What are the key market trends
What is driving Global Agricultural AI market
What are the challenges to market growth
Who are the key vendors in Global Agricultural AI market space?
What are the key market trends impacting the growth of the Global Agricultural AI market?
What are the key outcomes of the five forces analysis of the Global Agricultural AI market?
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There are 15 Chapters to display the Global Agricultural AI market.
Chapter 1, to describe Definition, Specifications and Classification of Agricultural AI , Applications of Agricultural AI , Market Segment by Regions;
Chapter 2, to analyze the Manufacturing Cost Structure, Raw Material and Suppliers, Manufacturing Process, Industry Chain Structure;
Chapter 3, to display the Technical Data and Manufacturing Plants Analysis of Agricultural AI , Capacity and Commercial Production Date, Manufacturing Plants Distribution, R&D Status and Technology Source, Raw Materials Sources Analysis;
Chapter 4, to show the Overall Market Analysis, Capacity Analysis (Company Segment), Sales Analysis (Company Segment), Sales Price Analysis (Company Segment);
Chapter 5 and 6, to show the Regional Market Analysis that includes United States, EU, Japan, China, India & Southeast Asia, Agricultural AI Segment Market Analysis (by Type);
Chapter 7 and 8, to analyze the Agricultural AI Segment Market Analysis (by Application) Major Manufacturers Analysis of Agricultural AI ;
Chapter 9, Market Trend Analysis, Regional Market Trend, Market Trend by Product Type [Machine Learning, Computer Vision & Predictive Analytics], Market Trend by Application [Precision Farming, Livestock Monitoring, Drone Analytics, Agriculture Robots & Others];
Chapter 10, Regional Marketing Type Analysis, International Trade Type Analysis, Supply Chain Analysis;
Chapter 11, to analyze the Consumers Analysis of Agricultural AI ;
Chapter 12, to describe Agricultural AI Research Findings and Conclusion, Appendix, methodology and data source;
Chapter 13, 14 and 15, to describe Agricultural AI sales channel, distributors, traders, dealers, Research Findings and Conclusion, appendix and data source.
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